Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.

单细胞 RNA 测序 (scRNA-seq) 技术允许在转录组水平上对数千个细胞进行大规模并行表征。scRNA-seq 正在成为研究细胞成分及其在肿瘤微环境中相互作用的重要工具。scRNA-seq 还用于揭示肿瘤微环境模式与临床结果之间的关联，并剖析复杂组织中药物治疗的细胞特异性效应。scRNA-seq 的最新进展推动了疾病和治疗靶标中生物标志物的发现。尽管已经提出了使用 scRNA-seq 数据的基因表达来预测药物反应的方法，但是需要一个从 scRNA-seq 分析到药物发现的集成工具。我们将 scDrug 呈现为一种生物信息学工作流程，其中包括为 scRNA-seq 数据生成细胞聚类的一步管道和两种预测药物治疗的方法。scDrug 管道由三个主要模块组成：用于识别肿瘤细胞亚群的 scRNA-seq 分析、细胞亚群的功能注释和药物反应预测。scDrug 可以轻松探索 scRNA-seq 数据，并促进药物再利用过程。scDrug 可在 GitHub 上免费获得，网址为 https://github.com/ailabstw/scDrug。scDrug 可以轻松探索 scRNA-seq 数据，并促进药物再利用过程。scDrug 可在 GitHub 上免费获得，网址为 https://github.com/ailabstw/scDrug。scDrug 可以轻松探索 scRNA-seq 数据，并促进药物再利用过程。scDrug 可在 GitHub 上免费获得，网址为 https://github.com/ailabstw/scDrug。